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首页> 外文会议>International Conference on E-Commerce and Web Technologies(EC-Web 2005); 20050823-26; Copenhagen(DK) >Knowledge Discovery in Web-Directories: Finding Term-Relations to Build a Business Ontology
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Knowledge Discovery in Web-Directories: Finding Term-Relations to Build a Business Ontology

机译：Web目录中的知识发现：查找术语关系以建立业务本体

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摘要

The Web continues to grow at a tremendous rate. Search engines find it increasingly difficult to provide useful results. To manage this explosively large number of Web documents, automatic clustering of documents and organising them into domain dependent directories became very popular. In most cases, these directories represent a hierarchical structure of categories and sub-categories for domains and sub-domains. To fill up these directories with instances, individual documents are automatically analysed and placed into them according to their relevance. Though individual documents in these collections may not be ranked efficiently, combinedly they provide an excellent knowledge source for facilitating ontology construction in that domain. In (mainly automatic) ontology construction steps, we need to find and use relevant knowledge for a particular subject or term. News documents provide excellent relevant and up-to-date knowledge source. In this paper, we focus our attention in building business ontologies. To do that we use news documents from business domains to get an up-to-date knowledge about a particular company. To extract this knowledge in the form of important "terms" related to the company, we apply a novel method to find "related terms" given the company name. We show by examples that our technique can be successfully used to find "related terms" in similar cases.
机译：Web继续以惊人的速度增长。搜索引擎发现提供有用的结果越来越困难。为了管理大量的Web文档，文档的自动聚类并将其组织到与域相关的目录中变得非常流行。在大多数情况下，这些目录代表域和子域的类别和子类别的层次结构。为了用实例填充这些目录，将自动分析各个文档并将其根据相关性放入其中。尽管这些集合中的单个文档可能无法有效地进行排名，但是它们的结合使用为在该领域促进本体构建提供了极好的知识来源。在（主要是自动的）本体构建步骤中，我们需要查找并使用针对特定主题或术语的相关知识。新闻文件提供了极好的相关和最新的知识来源。在本文中，我们将注意力集中在构建业务本体上。为此，我们使用来自业务领域的新闻文档来获取有关特定公司的最新知识。为了以与公司相关的重要“术语”的形式提取该知识，我们采用一种新颖的方法来查找给定公司名称的“相关术语”。通过示例显示，在相似的情况下，我们的技术可以成功地用于查找“相关术语”。

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